In [5]:
import yfinance as yf
import pandas as pd
import numpy as np
import seaborn as sns
from datetime import datetime
from datetime import timedelta
import plotly.graph_objects as go
from fbprophet import Prophet
from fbprophet.plot import plot_plotly, plot_components_plotly
import warnings
warnings.filterwarnings('ignore')
pd.options.display.float_format = '${:,.2f}'.format
In [8]:
#downloading ETH historical data from yahoo finance with yfinance library
today = datetime.today().strftime('%Y-%m-%d')
start_date = '2016-01-01'

eth_df = yf.download('ETH-USD', start_date, today)
eth_df.tail()

[*********************100%***********************]  1 of 1 completed
Out[8]:
Open High Low Close Adj Close Volume
Date
2022-04-08 $3,233.27 $3,301.61 $3,179.14 $3,192.07 $3,192.07 17557050669
2022-04-09 $3,191.98 $3,261.96 $3,187.47 $3,261.92 $3,261.92 9908112156
2022-04-10 $3,261.29 $3,303.00 $3,211.87 $3,211.87 $3,211.87 10427054790
2022-04-11 $3,209.58 $3,214.46 $2,962.76 $2,981.05 $2,981.05 21891804831
2022-04-12 $2,987.16 $3,076.92 $2,960.05 $3,023.27 $3,023.27 20259698688

Exploratory Data Analysis¶

In [9]:
eth_df.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1616 entries, 2017-11-09 to 2022-04-12
Data columns (total 6 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Open       1616 non-null   float64
 1   High       1616 non-null   float64
 2   Low        1616 non-null   float64
 3   Close      1616 non-null   float64
 4   Adj Close  1616 non-null   float64
 5   Volume     1616 non-null   int64  
dtypes: float64(5), int64(1)
memory usage: 88.4 KB
In [10]:
#check if we have any NAs
eth_df.isnull().sum()
Out[10]:
Open         0
High         0
Low          0
Close        0
Adj Close    0
Volume       0
dtype: int64
In [11]:
#check columns in a dataframe
eth_df.columns
Out[11]:
Index(['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')
In [12]:
#adding date column to the df/reindexing
eth_df.reset_index(inplace=True)

eth_df.columns
Out[12]:
Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')
In [16]:
#we only need 2 columns for our forecasting prophet model
df = eth_df[['Date', 'Open']]

new_names = {
    'Date': 'ds',
    'Open': 'y',
}

df.rename(columns=new_names, inplace = True)
In [17]:
#check if data is ready for prophet
df.tail()
Out[17]:
ds y
1611 2022-04-08 $3,233.27
1612 2022-04-09 $3,191.98
1613 2022-04-10 $3,261.29
1614 2022-04-11 $3,209.58
1615 2022-04-12 $2,987.16
In [18]:
#plot the open price
x = df['ds']
y = df['y']

fig = go.Figure()

fig.add_trace(go.Scatter(x=x, y=y))

#Set title
fig.update_layout(
title_text = 'Time series plot of Ethereum Open Price')

Prophet model¶

In [20]:
#fitting data into the Prophet model

m = Prophet(
seasonality_mode='multiplicative'
)

m.fit(df)

INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
Out[20]:
<fbprophet.forecaster.Prophet at 0x16a66261c10>
In [21]:
#now we can create an entire years worth of date data for our prophet model to make predictions
future = m.make_future_dataframe(periods=365)
future.tail()
Out[21]:
ds
1976 2023-04-08
1977 2023-04-09
1978 2023-04-10
1979 2023-04-11
1980 2023-04-12

Model Predictions¶

In [22]:
forecast = m.predict(future)
forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()
#one year in advance
Out[22]:
ds yhat yhat_lower yhat_upper
1976 2023-04-08 $3,795.84 $2,235.47 $5,274.51
1977 2023-04-09 $3,793.83 $2,271.73 $5,215.67
1978 2023-04-10 $3,784.79 $2,274.64 $5,246.04
1979 2023-04-11 $3,769.76 $2,263.95 $5,204.02
1980 2023-04-12 $3,775.60 $2,237.83 $5,215.04
In [23]:
#prediction for the next day
next_day = (datetime.today() + timedelta(days=1)).strftime('%Y-%m-%d')

#next day is 4/13/2022
forecast[forecast['ds'] == next_day]['yhat'].item()
Out[23]:
3055.09070239782

Forecast Plots¶

In [25]:
plot_plotly(m, forecast)

Forecast Components¶

In [26]:
#other components of our forecast model include trend, yearly, and weekly visualiation diagrams
plot_components_plotly(m,forecast)
In [ ]:
Our model tells us that:
    1. There will be an upward trend for the price of Ethereum
    2. The price of ETH is lowest in July and April on a Saturday.
    3. ETH is most expensive around November and May on a Thursday.